Biology subjects

Freddolino, L.

Publications and source records attributed to Freddolino, L..

18 recordsLinked to original sources

bioRxiv2026

Mu-seq reveals previously undetected long-range contact patterns in the Escherichia coli nucleoid

The spatial organization of the Escherichia coli chromosome is a key determinant of its functional and regulatory landscape, but widely used chromatin conformation capture methods, such as Hi-C, provide an incomplete view of the underlying three-dimensional genome connectivity. Here, we introduce Mu-seq, a massively multiplexed Mu transposition-based approach that maps genome-wide DNA-DNA proximity in live cells without crosslinking. By distributing engineered, barcoded Mu prophages throughout the chromosome and tracking their replicative transposition events, Mu-seq generates dense, uniform sampling of physical contacts across the genome, enabling a direct, in vivo snapshot of chromosome architecture. Applying this method in E. coli, we constructed a contact dataset spanning over 200,000 unique loci, revealing interaction patterns not accessible to current crosslinking-based techniques. Mu-seq findings can be broken down into three layers. First, it recapitulates polymer-like enrichment of short-range contacts. Second, contacts identified by Mu-seq are strongly shaped by chromatin state: transcriptionally active and accessible regions disproportionately contribute to long-range connectivity, whereas protein-occupied domains, including H-NS-enriched regions, are depleted of insertions and contacts. Third, beyond distance-dependent polymer behavior and chromatin accessibility, Mu-seq reveals specific long-range associations among functionally related loci, such as ribosomal operons. These findings suggest that bacterial chromosome organization can bring functionally related regions into shared three-dimensional neighborhoods even when they are widely separated along the linear genome. Together, these findings support a layered model of nucleoid organization, in which transcriptionally active loops extend outward from a dense, protein-rich axial core. Mu-seq thus provides a powerful, complementary in vivo framework for probing bacterial chromosome architecture, uncovering regulatory and structural features of genome organization that are largely invisible to crosslinking-based approaches.

microbiology↗

bioRxiv2026

Whole-proteome structure/function prediction in Uropathogenic E. coli reveals previously missed host-microbe and microbe-phage interaction pathways

The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our understanding of biological mechanisms of these proteins, hinders the interpretation of high-throughput experiments, and exposes a significant challenge in modern biology: deducing the structural and functional information of proteins based on their sequences. Most computational approaches rely on homology with well-annotated proteins, yet many proteins lack identifiable homologues, reducing the power of this approach. Here, we integrated cutting-edge protein structure and function prediction methods to develop a complete sequence-structure-function pipeline that predicts structures and functions based on primary sequences. We applied this pipeline to predict the structure and function of all proteins in Escherichia coli UTI89, a model strain of uropathogenic E. coli. Based on the predicted functions, we performed enrichment analysis on the whole genome and revealed the possible roles and related biological mechanisms of poorly annotated proteins in this organism. Moreover, the performance of our pipeline was further validated through detailed case studies of the UTI89_C0931 and ybtS genes. Finally, we compiled the UTI89 structure and function database (https://seq2fun.dcmb.med.umich.edu/UTI89), offering it as a community resource to aid researchers in elucidating the roles of unannotated proteins in uropathogenic E. coli. This database aims to bridge critical knowledge gaps in microbial pathogenicity and resistance, enhancing our capacity to tackle emerging health threats.

microbiology↗

bioRxiv2026

Defining the Candidozyma auris pan-genome and essentiality

Candidozyma auris is a multi-drug resistant fungal pathogen that causes hospital outbreaks, but genetic variation and gene function in this organism remain undercharacterized. Here, we performed pangenome analysis on 695 genetically representative isolates of C. auris and found that only 2.6% of gene families varied in their presence, mainly consisting of gene loss events or clade-specific genes, highlighting the clonal nature of these strains. Using microbial genome-wide association, we identified two loci associated with altered keratinocyte adherence, including genes with unknown function. To look broadly at gene function, we developed a genome-scale insertional mutagenesis approach and identified 614 high-confidence essential genes. Nearly one-third of these genes exhibited divergent essentiality compared to the model yeasts Candida albicans and Saccharomyces cerevisiae. Together, this study highlights organism-specific biology and provides a comprehensive resource for identifying species-specific determinants of virulence and targets for antifungal drug development.

microbiology↗

bioRxiv2026

3D chromatin compartment of round spermatids encodes the spatiotemporal program of histone-to-protamine exchange in spermiogenesis

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but its presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded "blueprint" that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

genomics↗

bioRxiv2025

The Algorithm for Reversible Jump Inference of Motifs

1Understanding sequence specificity in transcription factor binding is a critical step in understanding gene regulation. DNA-protein binding experiments such as ChIP-chip and ChIP-seq can reveal where on the genome a particular transcription factor of interest is bound, and similar experiments such as ATAC-seq or DNase hypersensitivity can reveal the profile of genome-wide protein binding and chromatin accessibility at the time of an experiment. However, demultiplexing the binding of multiple transcription factors represented in a single occupancy trace can be difficult. While many tools exist for extracting binding motifs from single transcription factor assays, comparatively few exist for deconvolving binding motifs from many transcription factor experiments, and they often require additional information on the number and/or nature of factors to be considered. Here, we developed The Algorithm for Reversible Jump Inference of Motifs (TARJIM), which translates DNA-protein binding data by inferring a set of sequence motifs that explain the data in question. By using a reversible jump Metropolis Hastings algorithm, TARJIM is able to infer both the number of motifs present and their sequence identity by using Bayesian techniques on the binding model itself. Using TARJIM, we have shown that not only can we deduce sequence motifs for known transcription factors, we are also able to extract sequence motifs from a mixture of sequence binding data, allowing us to extract information even from protein-DNA binding experiments where we do not know the number of transcription factors represented.

bioinformatics↗

bioRxiv2025

Enterococcus faecalis Influences the Transcription and Metabolism of Pathogenic Escherichia coli when Grown in Co-Culture

UTI involves bacterial growth in the disparate environments of the bladder and within uroepithelial cells. The bladder is a low-nutrient environment that nonetheless supports rapid growth. Phylogenetic group B2 Escherichia coli (Ec) is frequently isolated from urinary tract infection (UTI) patients. Non-B2 strains have also been isolated and are often co-isolated with Enterococcus faecalis (Ef). We characterized the interaction between three co-isolated Ec-Ef pairs and other Ec-Ef combinations in a nutrient-rich medium which was intended to emulate the rapid growth condition of the bladder. In this medium, Ef had little effect on Ec growth but resulted in major transcriptome differences. Ef affected the non-B2 and B2 strains differently. For the non-B2 Ec strains, Ef induced transcript for genes whose products degrade ornithine via putrescine to succinate which is subsequently metabolized by the TCA cycle. For a control B2 Ec strain, Ef induced transcripts for growth rate-associated genes of macromolecular synthesis, and for similar metabolic enzymes, except for those that degrade putrescine to succinate. Unexpectedly, Ef induced transcripts for glyoxylate shunt enzymes in both non-B2 and B2 Ec strains. The bacterial disparate and constantly changing environments during UTI suggest the potential for different types of Ec-Ef interactions. Our results provide evidence for nutrient cross-feeding as one type of Ec-Ef interaction.

microbiology↗

bioRxiv2025

The lac operon in uropathogenic Escherichia coli enhances intracellular growth by enabling host glycan utilization

The lac operon in Escherichia coli has been used as a model of gene regulation throughout biology since its characterization in the early 1960s. Despite the myriad of biotechnology applications that arose from characterization of the lac system, the explanation for the importance of a functional lac operon in normal bladder colonization by uropathogenic E. coli remains unknown given that the prototypical substrate, lactose, is not normally known to be readily available within the urinary tract. Here, we identified a unique uropathogenic clinical isolate (5.3r) that has a two codon deletion in the LacY permease, leading to impaired {beta}-galactoside metabolism and attenuation of development of critical intracellular bacterial communities (IBCs) in which UPEC replicates to high numbers, and then disseminates during urinary tract infection. Further, we show that expression of a functional lacY permease gene is sufficient to rescue defects in IBC size and number in 5.3r. In addition, we demonstrate that UPEC are able to utilize the disaccharide galactose {beta}-1,4 N-acetylglucosamine (LacNAc) - which appears as the terminal glycan in a subset of the glycoproteins that decorate the apical surface of bladder epithelial cells - as a sole carbon source in a lac system dependent manner. These data suggest that the lac operon is important to the growth and development of intracellular E. coli through metabolism of host bladder cell glycans.

microbiology↗

bioRxiv2025

LsrL modulates Lsr2-induced chromatin structure to tune biosynthetic gene cluster regulation in Streptomyces venezuelae

Specialized biosynthetic gene clusters in Streptomyces are subject to complex regulation involving both transcriptional control and chromosome organization. The nucleoid-associated protein Lsr2 silences many of these clusters, yet how it shapes the global chromatin structure and how its conserved paralog LsrL contributes to this process remain poorly understood. In this study, we applied a multi-omics approach, combining transcriptional activity, genome-wide protein-DNA binding profiles, and three-dimensional chromosome conformation to characterize the coordination of Lsr2 and LsrL in exerting transcriptional control and genome architecture in Streptomyces venezuelae. In line with established Lsr2 functions, we find that Lsr2 sets broad transcriptional boundaries, while LsrL acts in a more context-specific manner that depends on the presence of Lsr2 and may function to reinforce or modulate Lsr2-mediated silencing. Loss of Lsr2 reshaped the chromatin landscape genome-wide, relieving its restriction on short-range contacts, triggering strong transcriptional changes and new domain boundaries near de-repressed biosynthetic gene clusters. These findings establish Lsr2 as a dominant but contextually modulated regulator whose interplay with LsrL coordinates specialized metabolism with higher-order chromosome organization.

microbiology↗

bioRxiv2025

StoPred: Accurate Stoichiometry Prediction for ProteinComplexes Using Protein Language Models and GraphAttention

Proteins often function as part of complexes, and the specific stoichiometry of these assemblies is critical for their biological roles, but experimental determination of assembly composition remains challenging and existing computational methods for stoichiometry prediction are limited. Existing approaches rely on template-based searches or require predefined stoichiometry for structure prediction, hampering their applicability to proteins without close homologs or known assembly states. Recent advances using protein language models (pLM) have enabled sequence-based prediction of homo-oligomer stoichiometry, but these methods are not applicable to hetero-oligomeric complexes and do not fully leverage inter-subunit relationships. Here, we present StoPred, a method that predicts the stoichiometry of protein complexes by integrating pLM embeddings with a graph attention network to model subunit-level interactions. StoPred infers stoichiometry directly from sequence or structure features for both homo-and hetero-oligomers, without requiring template assemblies or predefined composition. We benchmark StoPred against deep learning-based and template-based methods, and show that it achieves improved accuracy and efficiency across curated and blind datasets, with up to 16% and 41% higher top-1 accuracy for homomeric and heteromeric complexes, respectively, compared to the strongest prior method on our held-out test dataset. More importantly, StoPred is the first deep learning-based method capable of accurately predicting the stoichiometry of hetero-oligomeric complexes.

bioinformatics↗

bioRxiv2025

Conserved Heterochromatin-like Structures with Local Regulators Mediate the Iron Stress Response in Mycobacteria

Recent studies have demonstrated the importance of dynamic heterochromatin-like regions in bacterial gene regulation, particularly for adaptation to changing environments. Here, we have measured the dynamic regulatory protein-DNA landscape of the tuberculosis vaccine strain, M. bovis BCG Pasteur, under the pathogenically-relevant condition of iron starvation. Our results capture for the first time the overall protein occupancy landscape of the genome of M. bovis BCG, identifying extended protein occupancy domains likely composed of diverse sets of nucleoid-associated proteins and transcription factors. Importantly, we find chromatin-directed regulation of stress-responsive genes like siderophores. Furthermore, through comparison with the free-living M. smegmatis, we identified a specific class of extended protein occupancy domains that are associated with conserved genomic regions across the two organisms, whereas regions with low protein occupancy often lack conservation. Our findings thus comprehensively reveal the contributions of both local regulators and chromatin structure to gene regulation and evolution in mycobacteria.

microbiology↗

bioRxiv2025

EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs

Features extracted from sequence homologs significantly enhance the accuracy of deep learning-based protein structure prediction. Indeed, models such as AlphaFold, which extracts features from sequence homologs, generally produce more accurate protein structures compared to single sequence-based methods like ESMfold. In contrast, features from sequence homologs are seldom employed for deep learning-based protein function prediction. Although a small number of models also incorporate function labels from sequence homologs, they cannot utilize features extracted from sequence homologs that lack function labels. To address this gap, we propose EZpred, which is the first deep learning model to use unlabeled sequence homologs for protein function prediction. Starting with the target sequence and homologs identified by MMseqs2, EZpred extracts sequence features using the ESMC protein language model. These features are then fed into a deep learning model to predict the Enzyme Commission (EC) numbers of the target protein. For 753 enzymes, the F1-score of EZpred EC number prediction is 4% higher than a similar model that does not use sequence homologs and at least 10% higher that state-of-the-art EC number prediction models. These results demonstrate the strong positive impact of sequence homologs in deep learning-based enzyme function prediction. Significance StatementMultiple sequence alignment (MSA) of homologous sequences is the most important source of input feature for deep learning-based protein structure prediction. Yet, this kind of feature is rarely used for protein function prediction. We propose the first deep learning model that significantly improves protein function prediction using features from sequence homologs without functional labels. This shows the utility of an important set of features that are often overlooked by previous studies.

bioinformatics↗

bioRxiv2025

HP-Bodies - Ancestral Condensates that Regulate RNA Turnover and Protein Translation in Bacteria

Uncovering what drives select biomolecules to form phase-separated condensates in vivo and identifying their physiological significance are topics of fundamental importance. Here we show that nitrogen-starved Escherichia coli produce long-chain polyphosphates, which scaffold the RNA chaperone Hfq into phase-separating high molecular weight complexes together with components of the RNA translation and processing machinery. The presence of polyphosphate within these condensates, which we termed HP-bodies, controls Hfq function by selectively stabilizing polyadenylated RNAs involved in transcription and protein translation, and promoting interactions with translation- and RNA-metabolism-associated proteins involved in de novo protein synthesis. Lack of polyphosphate prevents HP-body formation, which increases cell death and significantly hinders recovery from N-starvation. In functional analogy, we demonstrate that polyP contributes specifically to the formation of Processing (P)-bodies in human cell lines, revealing that a single, highly conserved and ancestral polyanion serves as the universal scaffold for functional phase-separated condensate formation across the tree of life.

microbiology↗

bioRxiv2025

ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions

Determining where transcriptional regulators bind within a genome is paramount to understanding how gene expression is regulated. Historically, position weight matrices (PWMs) have been used to define the binding preferences of DNA binding proteins1. However, PWMs treat the identity of each base in a sequence as an independent and additive measure of binding preference, which can limit their utility2. Models that consider higher order interactions between nearby bases yield greater success in predicting proteins binding to DNA, but for many proteins there is still substantial room for improvement in predicting and understanding the determinants of proteins binding to DNA3. In addition to DNA sequence motifs, structural motifs (e.g., a narrow minor groove width) are important determinants of binding for some DNA-binding proteins4. Despite the initial success of algorithms using structural features of DNA to predict binding properties of proteins from either ChIP-seq or SELEX data5-8, there remains a need for a de novo structural motif discovery framework which can be applied to data from a variety of experimental designs. Here, we present a unified workflow, capable of utilizing virtually any type of data representing sequence coverage or enrichment (e.g. ChIP-seq, RNA-seq, SELEX, etc.), to discover short structural motifs with explanatory power for a proteins DNA binding preference. We couple the DNAshapeR algorithm9 with our own information-theoretic approach to de novo motif discovery, and wrap shape and sequence motif inference and model selection into a single tool called ShapeME. Application of our structural motif discovery algorithm to proteins with ChIP-seq data in ENCODE datasets reveals a subset of proteins where short structural motifs outperform the best PWM for that protein as determined from the JASPAR database, or as identified by the sequence motif elicitation tool STREME. Our approach offers a powerful and versatile framework for inferring structural DNA binding motifs, and will complement current sequence-based motif elicitation tools in discovery of protein-DNA interaction principles. A web-based interface to ShapeME is available at https://seq2fun.dcmb.med.umich.edu/shapeme, with full source code available at https://github.com/freddolino-lab/ShapeME.

bioinformatics↗

bioRxiv2025

DNA methylation affects gene expression but not global chromatin structure in Escherichia coli

The activity of DNA adenine methyltransferase (Dam) and DNA cytosine methyltransferase (Dcm) together account for nearly all methylated nucleotides in the Escherichia coli K-12 MG1655 genome. Previous studies have shown that perturbation of DNA methylation alters E. coli global gene expression, but it is unclear whether the methylation state of Dam or Dcm target sites regulates local transcription. In recent genome-wide experiments, we observed an underrepresentation of Dam sites in transcriptionally silent extended protein occupancy domains (EPODs), prompting us to hypothesize that EPOD formation is caused partially by low Dam site density. We thus hypothesized that a methylation-deficient version of MG1655 would show large-scale aberrations in chromatin structure. To test our hypothesis, we cloned methyltransferase deletion strains and performed global protein occupancy profiling using high resolution in vivo protein occupancy display (IPOD-HR), chromatin immunoprecipitation for RNA polymerase (RNAP-ChIP), and transcriptome abundance profiling using RNA-Seq. Our results indicate that loss of DNA methylation does not result in large-scale changes in genomic protein occupancy such as the formation of EPODs, indicating that the previously observed depletion of Dam sites in EPODs is correlative, rather than causal, in nature. However, loci with dense clustering of Dam methylation sites show methylation-dependent changes in local RNA polymerase and total protein occupancy, but local transcription is unaffected. Our transcriptome profiling data indicates that deletion of dam and/or dcm results in significant expression changes within some functional gene categories including SOS response, flagellar synthesis, and translation, but these expression changes appear to result from indirect regulatory consequences of methyltransferase deletion. In agreement with the downregulation of genes involved in flagellar synthesis, dam deletion is characterized by a swimming motility-deficient phenotype. We conclude that DNA methylation does not control the overall protein occupancy landscape of the E. coli genome, and that observable changes in gene regulation are generally not resulting from regulatory consequences of local methylation state. IMPORTANCEPrevious studies of E. coli chromatin structure revealed a statistical association between the presence of silenced, highly protein occupied regions of the genome and depletion of modification sites for Dam methyltransferase. Here, we show that loss of DNA methylation does not substantively affect global chromatin structure in E. coli, thus demonstrating that the previously observed correlation was not causal. However, we observed specific methylation-dependent changes in gene expression, particularly affecting the SOS response, flagellar synthesis, and translation. These effects appear to be indirect regulatory consequences of methyltransferase deletion. Our work clarifies the role of methylation in chromatin structure and regulation, providing new insights into the mechanistic basis of gene expression and chromatin structure in E. coli.

systems biology↗

bioRxiv2024

Negative feedback of cyclic di-GMP levels optimizes switching between sessile and motile lifestyles in Vibrio cholerae

The signaling molecule cyclic di-GMP (c-di-GMP) controls the switch between bacterial motility and biofilm production, and fluctuations in cellular levels of c-di-GMP have been implicated in Vibrio cholerae pathogenesis. Intracellular concentrations of c-di-GMP are controlled by the interplay of diguanylate cyclase (DGC) enzymes, which synthesize c-di-GMP to promote biofilms, and phosphodiesterase (PDE) enzymes, which hydrolyze c-di-GMP to drive motility. To track the complete regulatory logic of how V. cholerae responds to changing c-di-GMP levels, we followed a time course of overexpression of either the V. campbellii diguanylate cyclase QrgB or a variant of QrgB lacking catalytic activity (QrgB*). We find that QrgB increases c-di-GMP levels relative to QrgB* for 30 minutes after overexpression, but the effect of QrgB on c-di-GMP levels plateaus at 30 minutes, indicating tight adaptive control of c-di-GMP levels. In contrast, loss of VpsR, a master regulator activating biofilm formation upon binding to c-di-GMP, leads to higher baseline levels of c-di-GMP and continuously increasing c-di-GMP through 60 minutes after QrgB induction, revealing the existence of a negative feedback loop on c-di-GMP levels operating through VpsR. Through a combination of RNA polymerase ChIP-seq, RNA-seq, and genetic approaches, we show that transcription of a gene encoding a PDE, cdgC, is activated by VpsR at high c-di-GMP concentrations, mediating this negative feedback on c-di-GMP levels. Further, although cells lacking cdgC exhibit enhanced biofilm formation, these mutants are outcompeted by wild type V. cholerae in colonization assays that reward a combination of attachment, dispersal, and motility behaviors. These results underscore the importance of negative feedback regulation of c-di-GMP to maintain appropriate homeostatic levels for efficient transitioning between biofilm formation and motility, both of which are necessary over the course of the V. cholerae infection cycle.

microbiology↗

bioRxiv2024

InterLabelGO+: Unraveling label correlations in protein function prediction

MotivationAccurate protein function prediction is crucial for understanding biological processes and advancing biomedical research. However, the rapid growth of protein sequences far outpaces the experimental characterization of their functions, necessitating the development of automated computational methods. ResultsWe present InterLabelGO+, a hybrid approach that integrates a deep learning-based method with an alignment-based method for improved protein function prediction. InterLabelGO+ incorporates a novel loss function that addresses label dependency and imbalance and further enhances performance through dynamic weighting of the alignment-based component. A preliminary version of InterLabelGO+ achieved a strong performance in the CAFA5 challenge, ranking 6th out of 1,625 participating teams. Comprehensive evaluations on large-scale protein function prediction tasks demonstrate InterLabelGO+s ability to accurately predict Gene Ontology terms across various functional categories and evaluation metrics. Availability and ImplementationThe source code and datasets for InterLabelGO+ are freely available on GitHub at https://github.com/QuanEvans/InterLabelGO. The software is implemented in Python and PyTorch, and is supported on Linux and macOS. Contactlydsf@umich.edu (LF) and zcx@umich.edu (CZ)

bioinformatics↗

bioRxiv2024

StarFunc: fusing template-based and deep learning approaches for accurate protein function prediction

Deep learning has significantly advanced the development of high-performance methods for protein function prediction. Nonetheless, even for state-of-the-art deep learning approaches, template information remains an indispensable component in most cases. While many function prediction methods use templates identified through sequence homology or protein-protein interactions, very few methods detect templates through structural similarity, even though protein structures are the basis of their functions. Here, we describe our development of StarFunc, a composite approach that integrates state-of-the-art deep learning models seamlessly with template information from sequence homology, protein-protein interaction partners, proteins with similar structures, and protein domain families. Large-scale benchmarking and blind testing in the 5th Critical Assessment of Function Annotation (CAFA5) consistently demonstrate StarFuncs advantage when compared to both state-of-the-art deep learning methods and conventional template-based predictors.

bioinformatics↗

bioRxiv2024

Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae

Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches it occupies. H-NS, the histone-like nucleoid structuring protein, is the best studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified a few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs. ImportanceNew strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. Proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.

microbiology↗